Near real-time visualizations for intelligent virtual assistant responses

ABSTRACT

A real-time conversation is monitored between a user and an intelligent virtual assistant (IVA). A visualization may be generated and displayed to the user on the user computing device based on one or more topics identified in the conversation. The conversation between the user and the IVA may continue and is continued to be monitored. The visualization can be updated as the conversation continues, e.g., based on further topics being identified.

BACKGROUND

Currently, intelligent virtual assistants (IVAs) and chatbots either donot know about precise real-time data during an IVA session, and at bestdisplay a link to an external website that will provide thatinformation, or simply provide data that was current at the time the IVAinputs and responses were last updated.

Thus, an IVA may not be able to provide the information the userimmediately needs or desires, resulting in an unsatisfactory userexperience. It is desirable to provide the real-time information orother content that a user wants, so the user does not have to leave theIVA to find that information or content.

SUMMARY

A real-time conversation is monitored between a user and an intelligentvirtual assistant (IVA). A visualization may be generated and displayedto the user on the user computing device based on one or more topicsidentified in the conversation. The conversation between the user andthe IVA may continue and is continued to be monitored. The visualizationcan be updated as the conversation continues, e.g., based on furthertopics being identified.

Systems and methods are provided for creating an additional responseservice within an IVA that can dynamically generate visualizations basedon the current topic of conversation with a specific user. Dynamicgraphs are produced of externally queried real-time data sources thatare relevant to the current topic of conversation with an IVA.

In an implementation, a system for providing a visualization includes anintelligent virtual assistant (IVA) configured to receive naturallanguage processing (NLP) inputs from a user computing device; and acomputing device in communication with the IVA and configured togenerate a visualization based on the NLP inputs and provide thevisualization for display on the user computing device.

In an implementation, method for providing a visualization includesmonitoring a conversation between a user computing device and anintelligent virtual assistant (IVA); generating a visualization based ona topic in the conversation; and providing the visualization for displayon the user computing device.

In an implementation, a system for providing a visualization includes avisualization determination module configured to monitor a conversationbetween a user computing device and an intelligent virtual assistant(IVA); a data gathering module configured to retrieve data from at leastone of an internal data source or an external data source based on atopic in the conversation; a data processing module configured toprocess the retrieved data; and a visualization generation moduleconfigured to generate a visualization based on the processed data andprovide the visualization for display on the user computing device.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theembodiments, there is shown in the drawings example constructions of theembodiments; however, the embodiments are not limited to the specificmethods and instrumentalities disclosed. In the drawings:

FIG. 1 is an illustration of an exemplary environment for real-time ornear real-time visualizations for intelligent virtual assistant (IVA)responses;

FIG. 2 is an operational flow of an implementation of a method ofproviding a visualization to a user computing device during aconversation between a user and an IVA;

FIG. 3 is an operational flow of another implementation of a method ofproviding a visualization to a user computing device during aconversation between a user and an IVA;

FIG. 4 is an operational flow of another implementation of a method ofproviding a visualization to a user computing device during aconversation between a user and an IVA; and

FIG. 5 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented.

DETAILED DESCRIPTION

FIG. 1 is an illustration of an exemplary environment 100 for real-timeor near real-time visualizations 198 for intelligent virtual assistant(IVA) 157 responses. It is determined that a visualization 198 is to begenerated and displayed based on topic identification (e.g., weather,finance, location, etc.). The topic is identified from conversationbetween a user 102 and the IVA 157. It is determined if the topic hadbeen predetermined to have a visualization (e.g., if there isvisualization information for the topic). If so, then the information isretrieved or otherwise determined or obtained for the topic and avisualization 198 is generated. The visualization 198 is then displayedon the user's display. In some implementations, if the conversation getsescalated to an agent (from the IVA 157), then the visualization 198 mayalso be displayed on the display.

The visualizations 198 may be dynamic and may be take the form of graphsor other representations, depending on the implementation. Thevisualizations 198 may be generated based on queried internal and/orexternal data sources (real-time and/or non-real-time) that are relevantto the current topic of conversation between a user 102 and the IVA 157.In this manner, the user 102 is provided with the relevant informationin a conversation with an IVA 157 so the user 102 can complete theirtransaction, rather than navigating the user 102 to an informationalpage within a website or giving the user 102 a link to an externalwebsite thereby decreasing the user's focus with the IVA 157 anddecreasing the likelihood of the user 102 completing their transaction.

A user 102, using a user computing device 105, such as a smartphone, atablet, or a laptop computer, communicates with an assistant computingdevice 155 associated with an entity 152 through a network 108 (e.g.,the Internet). The entity 152 may be an individual or a business,company, or organization, for example, that provides a product orservice, or access to a product or service, to the user 102 via thenetwork 108.

The user 102 may communicate with the assistant computing device 155 viathe user computing device 105 and an intelligent virtual assistant (IVA)157 of the assistant computing device 155. The user computing device 105(and thus the user 102) may interact with the IVA 157 using naturallanguage processing (NLP) associated with, or implemented by, the IVA157. Any known NLP methods may be used to interact with the user 102 andto determine the current topic of conversation between the user 102 (andthe user computing device 105) and the IVA 157.

A computing device 110 may be in communication with the assistantcomputing device 155 and/or the user computing device 105 to monitor thespeech in a voice call (i.e., the conversation) or other communicationbetween the user computing device 105 and the assistant computing device155 (e.g., the IVA 157). The computing device 110 may be implemented in,or embodied in, a desktop analytics product or in a speech analyticsproduct, in some implementations.

The computing device 110 may include a visualization determinationmodule 112, a data gathering module 114, a data processing module 116,and a visualization generation module 118. In some implementations, thecomputing device 110 may be comprised within the assistant computingdevice 155. In some implementations, one or more of the visualizationdetermination module 112, the data gathering module 114, the dataprocessing module 116, and the visualization generation module 118 maybe comprised within the assistant computing device 155 or anothercomputing device (not shown).

The network 108 may be a variety of network types including the publicswitched telephone network (PSTN), a cellular telephone network, and apacket switched network (e.g., the Internet). Although only one usercomputing device 105, one assistant computing device 155, and onecomputing device 110 are shown in FIG. 1 , there is no limit to thenumber of computing devices 105, 155, 110 that may be supported.

The user computing device 105, the assistant computing device 155, andthe computing device 110, may each be implemented using a variety ofcomputing devices such as smartphones, desktop computers, laptopcomputers, tablets, set top boxes, vehicle navigation systems, and videogame consoles. Other types of computing devices may be supported. Asuitable computing device is illustrated in FIG. 5 as the computingdevice 500.

In some implementations, the computing device 110 is in communicationwith, and is configured to receive data from, one or more internal datasources 160 and/or or more external data sources 170. The IVA 157 maycover a variety of media channels in addition to voice, including, butnot limited to social media, email, SMS/MMS, IM, etc. Additionally, theassistant computing device 155 can access and interact with one or morewebsites as additional sources of real-time data and/or non-real-timedata that can be used in generating a visualization 198.

The visualization determination module 112 is provided and configured todetermine whether a relevant visualization 198 should be created for thegiven user input based on the current topic of conversation. If so, avisualization generation module 118 is provided and configured togenerate the visualization 198 and provide the visualization 198 to theuser computing device 105 for display to the user 102 during theconversation between the user 102 (via the user computing device 105)and the IVA 157.

The data gathering module 114 is configured to gather, obtain, orotherwise receive data from the internal data sources 160 and/or theexternal data sources 170 based on the topic identified by thevisualization determination module. The internal data sources 160 maycomprise one or more of email 162, live chat logs, call center audiotranscripts 165, website search queries 166, and customer servicechannels 168, for example. The external data sources 170 may comprisesources of weather data 171, travel data 172, retail data 173, servicedata 174, transportation data 175, financial data 176, health andwellness data 177, inventory data 178, and restaurant data 179, forexample.

The data processing module 116 of the computing device 110 processes thedata retrieved from the data gathering module 114 to obtain the properdata from the retrieved data (e.g., remove redundancies, removeirrelevant data, etc.) and/or put the data into proper format forsubsequent visualization generation and provides the processed data tothe visualization generation module 118.

The visualization generation module 118 of the computing device 110generates a visualization 198 using the processed data and provides thevisualization 198 to the user computing device 105 for display to theuser. The visualization 198 may be displayed for a predetermined lengthof time or until a predetermined event occurs, for example.

The data provided in the visualizations 198 may comprise non-real-timedata from external data sources (e.g., historical weather data,historical financial data, etc.) and/or real-time and/or non-real-timedata from internal data sources. Data that may be relevant to theconversation, regardless of whether real-time or non-real-time, and fromexternal data sources or internal data sources, may be obtained andincorporated into a visualization 109 that is displayed to the user 102(e.g., on a display of the computing device 105 that the user 102 isusing to communicate with the IVA 157). For example, for a weatherstation, an API may be implemented that goes to the appropriate relevantURL and the real-time data may thus be obtained from that external datasource directed to weather data 171, and a visualization 198 (e.g., oneor more graphs) may be generated and displayed.

In some implementations, such as when as conversation gets passed fromthe IVA 157 to an agent of the entity who may be using the assistantcomputing device 155 (or another computing device), the computing device110 may also provide the visualization 198 to the assistant computingdevice 155 (or another computing device) for display to the agent who ishaving the conversation with the user 102 (via the user computing device105).

In some implementations, the conversation may be continuously monitored(or periodically monitored, or randomly monitored, depending on theimplementation) and the visualization 198 may be updated based on thetopic(s) that are identified during this continuous (or periodic, orrandom) monitoring.

Aspects of the invention are relevant as an add-on to IVA/IVR(intelligent virtual assistant/interactive voice response) services, asa service is provided that presents visualizations and/or other contentto users based on real-time data (and/or non-real-time data) fromexternal data sources (and/or internal data sources).

In some implementations, during the response generation phase of an IVA,the system will determine whether a data source (e.g., one or more ofthe internal data sources 160 or the external data sources 170) existsfor generation of a relevant visualization 198 to the selected response,using the context and/or topic of the conversation. If a data sourceexists, the response generation phase will generate a visualization 198of the data in a form that is optimal for the current response anddisplay the visualization 198 to the user along with a text and/or audioresponse, if any.

Some aspects may be implemented as an IVA client on the user computingdevice 102, communicating to the existing IVA 157 back end platform anddatabase. Instead of just a textual or audio response generation servicefor creating responses to user inputs to the IVA 157, there is anadditional visualization generation service (e.g., comprising thevisualization determination module 112, the data gathering module 114,the data processing module 116, and the visualization generation module118) that determines whether a relevant visualization should be createdfor the given user input. This may be implemented as a platform API onthe back end service that would field known queries via API and plug-insto known public (or private/paid) services or programs that couldgenerate a current dynamic weather, financial, currency exchange, healthand wellness, inventory status, restaurant status, etc. or any querythat is likely to be of more use to the user if it were current data,due to changing conditions over time, as opposed to a static databaseresult such as pre-generated images.

An example is when considering travel, on a travel related IVA 157 orchatbot, a user 102 may have questions relating to conditions such asweather, state department, or CDC warnings, etc. Rather than navigatingthe user 102 to an informational page within the current website orgiving them a link to an external website, the idea is to keep the focusof the user 102 within the IVA 157, while providing relevant informationto the user 102, so they can complete their transaction.

One example is the current COVID-19 situation. Because the situation indifferent regions is changing daily, it is important for a traveler toknow what the current restrictions and safety levels. Many travel,retail, service, or transportation IVAs will have static text statingthat conditions may change, and urge the user to do their own research.

By enabling tools written by programmers which access publicly availablestatistics, such as up to the hour death or infection rates of COVID-19in different states, and dynamically graphing the trend as part of theresponse to a user question on the subject, the user can make aninformed decision as to whether the situation, based on up to the hourdata, is favorable to continue the transaction.

While a travel website may have a weather tool showing the weatherforecast for the proposed trip dates, it does not have aconversationally-customized comparison, or plotted chart of averagesthat a software program running on weather data would be able toprovide. Providing this kind of deeper data and comparisons, withouthaving to leave the IVA 157 conversational window, can keep the focus ofthe user 102 on the pending transaction within the IVA 157 withoutresorting to directing them to links to other information sources suchas weather websites, and hoping the user 102 returns to the IVA 157 tocomplete the transaction.

The types of IVAs that this may apply include, for example and withoutlimitation: (1) travel, or travel and health conditions as the aboveexample indicates; (2) financial trading or portfolio management, wherecustom charts of current and historical data impact decisions can bedrawn based on user queries; (3) retail comparisons of pricing, returnpolicies, third party review sources; and (4) health or medical IVAswhere drug interactions can be looked up without leaving the IVA 157.

FIG. 2 is an operational flow of an implementation of a method 200 ofproviding a visualization 198 to a user computing device 105 during aconversation between a user 102 and an IVA 157.

At 210, a conversation is established between the user 102 (and the usercomputing device 105 and the IVA 157. At 220, the conversation ismonitored by the visualization determination module 112 for detection ofat least one predetermined topic. Alternatively or additionally, in someimplementations, the conversation may be monitored for context and/orsentiment, and visualizations may be generated and displayed responsiveto detection of context and/or sentiment along with or instead ofidentified topic. At 230, at least one predetermined topic is identifiedin the conversation, e.g., by the visualization determination module112.

At 240, information pertaining to the identified topic is retrieved orotherwise obtained from the data source(s) that had been associated withthat topic previously (e.g., by an administrator or other userassociated with the entity 152 or the IVA 157). Each data source may bereal-time or non-real-time, and may be an internal data source or anexternal data source. The information (i.e., the real-time and/ornon-real-time data) may be retrieved or otherwise obtained by the datagathering module 114. The retrieved data may then processed by the dataprocessing module 116 if appropriate depending on the implementation.

At 250, a visualization (or visualizations) are created (i.e.,generated) by the visualization generation module 118, and transmittedor otherwise provided to the user computing device 105 for display at260.

FIG. 3 is an operational flow of another implementation of a method 300of providing a visualization 198 to a user computing device 105 during aconversation between a user 102 and an IVA 157.

At 310, similar to 240, information pertaining to an identifiedpredetermined topic (during a live or real-time conversation between auser 102 and an IVA 157) is retrieved or otherwise obtained from thedata source(s) that had been associated with that topic previously(e.g., by an administrator or other user associated with the entity 152or the IVA 157). Each data source may be real-time or non-real-time, andmay be an internal data source or an external data source. Theinformation (i.e., the real-time and/or non-real-time data) may beretrieved or otherwise obtained by the data gathering module 114. Theretrieved data may then processed by the data processing module 116 ifappropriate depending on the implementation.

At 320, similar to 250, one or more visualizations are created (i.e.,generated) by the visualization generation module 118, and transmittedor otherwise provided to the user computing device 105 for display at330.

At 340, the conversation is continued to be monitored. The monitoringmay be continuous, periodic, or randomly timed, depending on theimplementation. As additional topics are identified, and/or the previoustopic (used in 310) is identified, the visualization may be updated at350 by creating a new and/or updated visualization (e.g., usingprocessing similar to that described with respect to 310, 320, 330). Thenew and/or update visualization may be displayed on the user computingdevice 105 as a replacement to the previously displayed visualization(e.g., from 330) or may be displayed on the user computing device 105 assupplemental to a previously displayed visualization, depending on theimplementation.

FIG. 4 is an operational flow of another implementation of a method 400of providing a visualization 198 to a user computing device 105 during aconversation between a user 102 and an IVA 157.

At 410, similar to 310, information pertaining to an identifiedpredetermined topic (during a live or real-time conversation between auser 102 and an IVA 157) is retrieved or otherwise obtained from thedata source(s) that had been associated with that topic previously(e.g., by an administrator or other user associated with the entity 152or the IVA 157). Each data source may be real-time or non-real-time, andmay be an internal data source or an external data source. Theinformation (i.e., the real-time and/or non-real-time data) may beretrieved or otherwise obtained by the data gathering module 114. Theretrieved data may then processed by the data processing module 116 ifappropriate depending on the implementation.

At 420, similar to 320, one or more visualizations are created (i.e.,generated) by the visualization generation module 118, and transmittedor otherwise provided to the user computing device 105 for display at430.

At 440, at some point, the conversation between the user 102 and the IVA157 is escalated to an agent (associated with the entity 152 or the IVA157). This may occur for any of a variety of known reasons and beimplemented using known techniques.

At 450, the visualization that was created at 420 and displayed on theuser computing device at 430 is also provided to, and displayed on, theagent computing device. In this manner, both the user and the agent areprovided with the visualization (and any updates to the visualization)on their respective displays during the conversation.

FIG. 5 shows an exemplary computing environment in which exampleembodiments and aspects may be implemented. The computing deviceenvironment is only one example of a suitable computing environment andis not intended to suggest any limitation as to the scope of use orfunctionality.

Numerous other general purpose or special purpose computing devicesenvironments or configurations may be used. Examples of well-knowncomputing devices, environments, and/or configurations that may besuitable for use include, but are not limited to, personal computers,server computers, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, network personal computers (PCs),minicomputers, mainframe computers, embedded systems, distributedcomputing environments that include any of the above systems or devices,and the like.

Computer-executable instructions, such as program modules, beingexecuted by a computer may be used. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Distributed computing environments may be used where tasks are performedby remote processing devices that are linked through a communicationsnetwork or other data transmission medium. In a distributed computingenvironment, program modules and other data may be located in both localand remote computer storage media including memory storage devices.

With reference to FIG. 5 , an exemplary system for implementing aspectsdescribed herein includes a computing device, such as computing device500. In its most basic configuration, computing device 500 typicallyincludes at least one processing unit 502 and memory 504. Depending onthe exact configuration and type of computing device, memory 604 may bevolatile (such as random access memory (RAM)), non-volatile (such asread-only memory (ROM), flash memory, etc.), or some combination of thetwo. This most basic configuration is illustrated in FIG. 5 by dashedline 506.

Computing device 500 may have additional features/functionality. Forexample, computing device 500 may include additional storage (removableand/or non-removable) including, but not limited to, magnetic or opticaldisks or tape. Such additional storage is illustrated in FIG. 5 byremovable storage 508 and non-removable storage 510.

Computing device 500 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by the device 500 and includes both volatile and non-volatilemedia, removable and non-removable media.

Computer storage media include volatile and non-volatile, and removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules or other data. Memory 504, removable storage508, and non-removable storage 510 are all examples of computer storagemedia. Computer storage media include, but are not limited to, RAM, ROM,electrically erasable program read-only memory (EEPROM), flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bycomputing device 500. Any such computer storage media may be part ofcomputing device 500.

Computing device 500 may contain communication connection(s) 512 thatallow the device to communicate with other devices. Computing device 500may also have input device(s) 514 such as a keyboard, mouse, pen, voiceinput device, touch input device, etc. Output device(s) 516 such as adisplay, speakers, printer, etc. may also be included. All these devicesare well known in the art and need not be discussed at length here.

It should be understood that the various techniques described herein maybe implemented in connection with hardware components or softwarecomponents or, where appropriate, with a combination of both.Illustrative types of hardware components that can be used includeField-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc. The methods and apparatus of the presently disclosedsubject matter, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, or any othermachine-readable storage medium where, when the program code is loadedinto and executed by a machine, such as a computer, the machine becomesan apparatus for practicing the presently disclosed subject matter.

In an implementation, a system for providing a visualization isprovided. The system includes: an intelligent virtual assistant (IVA)configured to receive natural language processing (NLP) inputs from auser computing device; and a computing device in communication with theIVA and configured to generate a visualization based on the NLP inputsand provide the visualization for display on the user computing device.

Implementations may include some or all of the following features. Thesystem further comprises at least one of an internal data source or anexternal data source, wherein the computing device is configured toreceive data from the internal data source or the external data sourceand generate the visualization based on the received data. The datacomprises real-time data. The internal data source comprises at leastone of email, live chat logs, call center audio transcripts, websitesearch queries, and customer service channels, and the external datasource comprises a source of at least one of weather data, travel data,retail data, service data, transportation data, financial data, healthand wellness data, inventory data, and restaurant data. The computingdevice is configured to provide the visualization for display on acomputing device of an agent. The NLP inputs relate to at least one of aplurality of predetermined topics. The computing device is configured toadjust the visualization based on further NLP inputs received from theuser computing device. The visualization is based on a first topic, andthe adjusted visualization is based on a second topic that is differentfrom the first topic. The visualization is based on a first topic, andthe adjusted visualization is based on the first topic.

In an implementation, a method for providing a visualization isprovided. The method includes: monitoring a conversation between a usercomputing device and an intelligent virtual assistant (IVA); generatinga visualization based on a topic in the conversation; and providing thevisualization for display on the user computing device.

Implementations may include some or all of the following features. Themethod further comprises establishing the conversation between the userand the IVA. The conversation comprises natural language processing(NLP) inputs from the user computing device. Monitoring the conversationbetween the user computing device and the IVA comprises determining thetopic in the conversation, wherein the topic is one of a plurality ofpredetermined topics. The method further comprises retrieving data fromat least one of an internal data source or an external data source basedon the topic, wherein generating the visualization comprises using theretrieved data. The data comprises real-time data. The internal datasource comprises at least one of email, live chat logs, call centeraudio transcripts, website search queries, and customer servicechannels, and the external data source comprises a source of at leastone of weather data, travel data, retail data, service data,transportation data, financial data, health and wellness data, inventorydata, and restaurant data. The method further comprises updating thevisualization for display on the user computing device based on thetopic or on another topic in the conversation. The method furthercomprises providing the visualization for display on a computing deviceof an agent.

In an implementation, a system for providing a visualization isprovided. The system includes: a visualization determination moduleconfigured to monitor a conversation between a user computing device andan intelligent virtual assistant (IVA); a data gathering moduleconfigured to retrieve data from at least one of an internal data sourceor an external data source based on a topic in the conversation; a dataprocessing module configured to process the retrieved data; and avisualization generation module configured to generate a visualizationbased on the processed data and provide the visualization for display onthe user computing device.

Implementations may include some or all of the following features. Thesystem further comprises at least one of an internal data source or anexternal data source configured to provide the data to the datagathering module, wherein the internal data source comprises at leastone of email, live chat logs, call center audio transcripts, websitesearch queries, and customer service channels, and wherein the externaldata source comprises a source of at least one of weather data, traveldata, retail data, service data, transportation data, financial data,health and wellness data, inventory data, and restaurant data.

Although exemplary implementations may refer to utilizing aspects of thepresently disclosed subject matter in the context of one or morestand-alone computer systems, the subject matter is not so limited, butrather may be implemented in connection with any computing environment,such as a network or distributed computing environment. Still further,aspects of the presently disclosed subject matter may be implemented inor across a plurality of processing chips or devices, and storage maysimilarly be effected across a plurality of devices. Such devices mightinclude personal computers, network servers, and handheld devices, forexample.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed:
 1. A system for providing a visualization, the systemcomprising: an intelligent virtual assistant (IVA) configured to receivenatural language processing (NLP) inputs from a user computing deviceduring an active IVA session comprising a real-time conversation with auser of the user computing device; a computing device in communicationwith the IVA and configured to generate the visualization based on theNLP inputs and provide the visualization for display on the usercomputing device by: determining, during the active IVA session, acurrent context and sentiment associated with at least a portion of theNLP inputs, identifying a data source for the visualization based atleast in part on the current context and sentiment, identifying avisualization format in accordance with the current context, generating,based on data received from the data source and during the active IVAsession, the visualization in the visualization format, in response todetecting a predetermined topic, retrieving additional data pertainingto the predetermined topic from the data source; and updating thevisualization based at least in part on the additional data, wherein thevisualization is continuously updated based on monitoring of the IVAsession; and a second user computing device in communication with theIVA and the user computing device configured to: in response toreceiving an escalation indication associated with the active IVAsession from the IVA, output a current visualization to the second usercomputing device.
 2. The system of claim 1, wherein the data sourcecomprises at least one of an internal data source or an external datasource.
 3. The system of claim 2, wherein the data comprises real-timedata.
 4. The system of claim 2, wherein the internal data sourcecomprises at least one of email, live chat logs, call center audiotranscripts, website search queries, and customer service channels, andwherein the external data source comprises a source of at least one ofweather data, travel data, retail data, service data, transportationdata, financial data, health and wellness data, inventory data, andrestaurant data.
 5. The system of claim 1, wherein the second usercomputing device is associated with an agent.
 6. The system of claim 1,wherein the NLP inputs relate to at least one of a plurality ofpredetermined topics.
 7. The system of claim 1, wherein the computingdevice is configured to adjust the visualization based on further NLPinputs received from the user computing device.
 8. The system of claim1, wherein the visualization is updated for a predetermined length oftime or until a detected occurrence of a predetermined event.
 9. Amethod for providing a visualization, the method comprising: monitoringa conversation between a user computing device and an intelligentvirtual assistant (IVA); determining, during the conversation, a currentcontext and sentiment associated with the conversation; identifying adata source for the visualization based at least in part on thedetermined context and sentiment; identifying a visualization format inaccordance with the current context; generating, during the conversationand based on data received from the data source, the visualization inthe visualization format based at least in part on a first predeterminedtopic in the conversation; providing the visualization for display onthe user computing device; in response to detecting a secondpredetermined topic, retrieving additional data pertaining to the secondpredetermined topic from the data source; updating the visualizationbased at least in part on the additional data, wherein the visualizationis continuously updated based on the monitoring of the conversation; andin response to receiving, by a second user computing device, anescalation indication associated with the conversation from the IVA,outputting a current visualization to the second user computing device.10. The method of claim 9, further comprising establishing theconversation between the user and the IVA.
 11. The method of claim 9,wherein the conversation comprises natural language processing (NLP)inputs from the user computing device.
 12. The method of claim 9,wherein monitoring the conversation between the user computing deviceand the IVA comprises determining the topic in the conversation, whereinthe topic is one of a plurality of predetermined topics.
 13. The methodof claim 12, wherein the data source comprises at least one of aninternal data source or an external data source.
 14. The method of claim13, wherein the data comprises real-time data.
 15. The method of claim13, wherein the internal data source comprises at least one of email,live chat logs, call center audio transcripts, website search queries,and customer service channels, and wherein the external data sourcecomprises a source of at least one of weather data, travel data, retaildata, service data, transportation data, financial data, health andwellness data, inventory data, and restaurant data.
 16. The method ofclaim 9, further comprising updating the visualization for display onthe user computing device based on the topic or on another topic in theconversation.
 17. The method of claim 9, wherein the second usercomputing device is associated with an agent.
 18. A system for providinga visualization, the system comprising: a visualization determinationmodule configured to: (i) monitor a conversation between a usercomputing device and an intelligent virtual assistant (IVA), and (ii)determine a current context and sentiment associated with theconversation; a data gathering module configured to: (i) identify aninternal data source or an external data source for the visualizationbased at least in part on the current context and sentiment, and (ii)retrieve data from at least one of the internal data source or theexternal data source based at least in part on a first predeterminedtopic in the conversation; a data processing module configured to: (i)identify a visualization format in accordance with the current contextand sentiment, and (ii) process the retrieved data; and a visualizationgeneration module configured to: (i) generate the visualization based onthe processed data in the visualization format, (ii) provide thevisualization for display on the user computing device, (iii) inresponse to detecting a second predetermined topic, retrieve additionaldata pertaining to the second predetermined topic from the internal datasource or the external data source, and (iv) update the visualizationbased at least in part on the additional data, wherein the visualizationis continuously updated based on monitoring of the conversation wherein:a second user computing device is in communication with the IVA and theuser computing device, and the second user computing device isconfigured to, in response to receiving an escalation indicationassociated with the conversation from the IVA, output a currentvisualization to the second user computing device.
 19. The system ofclaim 18, wherein the internal data source comprises at least one ofemail, live chat logs, call center audio transcripts, website searchqueries, and customer service channels, and wherein the external datasource comprises a source of at least one of weather data, travel data,retail data, service data, transportation data, financial data, healthand wellness data, inventory data, and restaurant data.